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          <h1 class="post-title" itemprop="name headline">使用Google免费GPU进行BERT模型fine-tuning</h1>
        

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        <blockquote>
<p>使用Google Colab中自带的免费GPU进行BERT fine-tuning。</p>
</blockquote>
<a id="more"></a>
<h3 id="前期准备"><a href="#前期准备" class="headerlink" title="前期准备"></a>前期准备</h3><p>首先，需要申请一个谷歌账号。</p>
<p>打开谷歌<a href="https://drive.google.com/" target="_blank" rel="noopener">云端硬盘</a>，新建一个文件夹，例如：BERT。将代码和数据上传到该文件里。这里的代码应该是已经修改好的代码，具体方法参照上一篇<a href="https://leowood.github.io/2018/11/16/BERT-fine-tuning-01/" target="_blank" rel="noopener">博客</a>，博客最后也有提到，谷歌Colab可以在运行的时候设定参数，因此这里代码里的参数可以保持为默认参数，方便在每次运行的时候修改。</p>
<p>在云盘任意位置新建-更多，选择Colaboratory：</p>
<p><img src="\images\google_new_colab.png" alt="1542370973659"></p>
<p><strong>如果在更多栏里没有发现Colaboratory，选择关联更多应用，搜索Colaboratory，选择关联。</strong> </p>
<p>创建完成后，会生成一个jupyter笔记本。</p>
<p>选择 修改-笔记本设置，将硬件加速器设置为GPU。</p>
<p>到这里，Google Colab已经配置完成，相当于一个远程的终端，我们可以在上面运行一些linux命令以及python代码，但是要调用谷歌云盘上面的数据和代码，还需要更多的配置。</p>
<h3 id="程序运行"><a href="#程序运行" class="headerlink" title="程序运行"></a>程序运行</h3><p>每次重新打开笔记本运行程序时，都要进行以下几步：</p>
<ul>
<li><p>安装相关库以及授权：</p>
<p>该步骤是为了建立笔记本与谷歌云盘的关联，具体来说就是给这个笔记本授权，使其能够访问你的谷歌云盘上面的文件。</p>
</li>
</ul>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br></pre></td><td class="code"><pre><span class="line">!apt-get install -y -qq software-properties-common python-software-properties module-init-tools</span><br><span class="line">!add-apt-repository -y ppa:alessandro-strada/ppa 2&gt;&amp;1 &gt; /dev/null</span><br><span class="line">!apt-get update -qq 2&gt;&amp;1 &gt; /dev/null</span><br><span class="line">!apt-get -y install -qq google-drive-ocamlfuse fuse</span><br><span class="line">from google.colab import auth</span><br><span class="line">auth.authenticate_user()</span><br><span class="line">from oauth2client.client import GoogleCredentials</span><br><span class="line">creds = GoogleCredentials.get_application_default()</span><br><span class="line">import getpass</span><br><span class="line">!google-drive-ocamlfuse -headless -id=&#123;creds.client_id&#125; -secret=&#123;creds.client_secret&#125; &lt; /dev/null 2&gt;&amp;1 | grep URL</span><br><span class="line">vcode = getpass.getpass()</span><br><span class="line">!<span class="built_in">echo</span> &#123;vcode&#125; | google-drive-ocamlfuse -headless -id=&#123;creds.client_id&#125; -secret=&#123;creds.client_secret&#125;</span><br></pre></td></tr></table></figure>
<p>运行后，会出现链接，点开链接，选择自己的谷歌账号，将得到的代码输入链接下面的框中即可。</p>
<p><img src="\images\google_auth.png" alt="1542371457391"></p>
<ul>
<li>挂载谷歌云端硬盘</li>
</ul>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">!mkdir -p drive</span><br><span class="line">!google-drive-ocamlfuse drive  -o nonempty</span><br></pre></td></tr></table></figure>
<p>每次修改了云盘中的文件以后都最好运行一次，运行了没有任何提示便成功了。</p>
<ul>
<li>设置工作路径</li>
</ul>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><span class="line">import os</span><br><span class="line">os.chdir(<span class="string">'drive/BERT'</span>)</span><br></pre></td></tr></table></figure>
<p>将笔记本的工作路径设置到BERT代码的文件夹下。</p>
<p>可以用ls命令查看是否成功：</p>
<p><img src="\images\ls_cmd.png" alt="1542371623670"></p>
<ul>
<li><p>运行run_classifier.py</p>
<p>使用命令直接运行位于云盘中的py文件，注意不要丢掉前面的感叹号。</p>
<p><strong>这里的\表示换行，如果py文件里面已经设置好了相关参数，下面参数可以不要。一旦选择用如下的方式进行参数设置，py文件中的FLAG参数设置必须都是默认参数，否则可能引起报错。</strong></p>
</li>
</ul>
<figure class="highlight bash"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br></pre></td><td class="code"><pre><span class="line">!python run_classifier.py \</span><br><span class="line">  --task_name=bert_move \</span><br><span class="line">  --do_train=<span class="literal">true</span> \</span><br><span class="line">  --do_eval=<span class="literal">true</span> \</span><br><span class="line">  --data_dir=data \</span><br><span class="line">  --vocab_file=gs://cloud-tpu-checkpoints/bert/uncased_L-24_H-1024_A-16/vocab.txt \</span><br><span class="line">  --bert_config_file=gs://cloud-tpu-checkpoints/bert/uncased_L-24_H-1024_A-16/bert_config.json \</span><br><span class="line">  --init_checkpoint=gs://cloud-tpu-checkpoints/bert/uncased_L-24_H-1024_A-16/bert_model.ckpt \</span><br><span class="line">  --max_seq_length=128 \</span><br><span class="line">  --train_batch_size=4 \</span><br><span class="line">  --learning_rate=2e-5 \</span><br><span class="line">  --num_train_epochs=3.0 \</span><br><span class="line">  --output_dir=output \</span><br></pre></td></tr></table></figure>
<p>其中gs://cloud-tpu-checkpoints/bert/uncased_L-12_H-768_A-12，是谷歌云中保存的bert预训练模型，我们无需自己上传，目前一共有：</p>
<p><img src="\images\bert_models.png" alt="1542371751932"></p>
<p>可以根据相关名称调用不同的模型，这里我们选择的是不区分大小写的基础模型，而大型模型uncased_L-24_H-1024_A-16，使用GPU暂无法运行。 </p>
<p>另外，因为前面已经设置了工作路径，因此这里data和output都是位于BERT文件目录下。output可以由程序自动创建，data文件夹需提前将train.tsv等文件上传进去。</p>

      
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